Classifying Hearing Device Receiver Based on Frequency Dependent Electrical Characteristics

This application claims the benefit of U.S. Provisional Application No. 63/710,266, filed Oct. 22, 2024, the disclosure of which is incorporated by reference herein in its entirety.

This application relates generally to ear-level electronic systems and devices, including hearing aids, personal amplification devices, and hearables. In one embodiment, an ear-wearable device includes receiver that outputs sound into an ear canal of a user in response to an electrical input signal. The device includes one or more electrical components separate from and electrically coupled to the receiver. A processor is configured to perform a measurement process involving sending an audio input signal to the receiver and measuring a set of frequency dependent electrical characteristics of the one or more electrical components in response to the audio input signal. The set of frequency dependent electrical characteristics are input into a machine learning model to determine an output. A classification of the receiver is determined based on the output of the machine learning model. The classification of the receiver is used to set an operational parameter of the ear-wearable device. In another embodiment, a method of processing sound in an ear-wearable

device involves sending an audio input signal to a receiver of the ear-wearable device and measuring a set of frequency dependent electrical characteristics of one or more electrical components in response to the audio input signal. The one or more electrical components are separate from and electrically coupled to the receiver. The set of frequency dependent electrical characteristics are input into a machine learning model to determine an output. A classification of the receiver is determined based on the output of the machine learning model. The classification of the receiver is used to set an operational parameter of the ear-wearable device. The figures and the detailed description below more particularly exemplify illustrative embodiments.

The figures are not necessarily to scale. Like numbers used in the figures refer to like components. However, it will be understood that the use of a number to refer to a component in a given figure is not intended to limit the component in another figure labeled with the same number.

Embodiments disclosed herein are directed to an ear-worn or ear-level electronic hearing device. Such a device may include cochlear implants and bone conduction devices, without departing from the scope of this disclosure. The devices depicted in the figures are intended to demonstrate the subject matter, but not in a limited, exhaustive, or exclusive sense. Ear-worn electronic devices (also referred to interchangeably herein as “hearing aids (HA),” “hearing devices,” “ear-wearable devices,” and “audio wearables (AW)”), such as hearables (e.g., wearable earphones, ear monitors, and earbuds), hearing aids, hearing instruments, and hearing assistance devices, typically include an enclosure, such as a housing or shell, within which internal components are mounted or disposed.

Embodiments described herein include features that provide automatic identification of a type of receiver (also referred to as a loudspeaker) currently installed in a hearing device. Generally, receivers can be user-replaceable in the event of a malfunction or failure of the receiver, for example. Other reasons to make receivers replaceable is to adapt to user changes, e.g., progression of hearing loss, discomfort with the current receiver, etc.

If a receiver is changed out, the audio processing electronics of the hearing device will typically have to change internal parameters if a new receiver has different properties than the existing receiver. Those internal parameters may include power output parameters (e.g., voltage output), frequency response, phase response, etc., due to different electrical characteristics of different receivers.

In order to identify and correctly configure a hearing device for a particular receiver, the receiver may currently be configured to communicate some information to the device electronics. For example, the receiver may include a small amount of memory that identifies the receiver, e.g., based on a model number, general classification, or the like. Such data can be transmitted over a data bus, e.g., inter-integrated circuit (I2C) bus. In other embodiments, a resistor network can be used to provide a similar function, e.g., setting resistance across one or more input-output pins to encode the receiver identification data.

While the use of memory or resistors to identify a receiver is effective, it adds costs. Not only do the memory or resistors themselves add to the cost of the receiver, additional conductors and/or communications interfaces add to the cost of both the receiver and the main board of the hearing device. Given the cost-competitiveness of the hearing aid market, it is desirable to reduce costs involved in receiver identification.

In embodiments described herein, one or more signals are sent to the receiver via its sound input channels. The inherent electrical characteristics of the receiver will affect the hearing device electronics in a specific way depending on the type and/or configuration of the receiver. The associated effects can be gathered as sensor information by existing sensors on the device. The sensor information (e.g., system voltage, system current, microphone signals, etc.) can be used by a machine learning algorithm to classify the receiver used.

A machine learning algorithm allows for the hearing device to identify the type of receiver being used by the hearing aid without any external memory or circuitry being used. This arrangement reduces the total overall cost of manufacturing a hearing device. For example, receiver-in-canal (RIC) assemblies will not need electrically-erasable, programmable read-only memory (EEPROM) and associated electrostatic discharge (ESD) protection components. This arrangement can be used on any hearing device with sufficient computing resources to run a small machine-learning model, e.g., a feedforward, neural network classifier, and can reduce component count and manufacturing cost.

1 FIG. 100 100 101 102 104 101 100 100 101 100 In, a block diagram illustrates an ear-wearable deviceaccording to an example embodiment. The deviceincludes a receiverthat outputs soundinto an ear canal in response to an electrical input signal. Note that the receiveris changeably attached to the car-wearable device, thus it is shown outside the box that defines the device. In the attached state, the receiveris considered as part of the car-wearable device, because it is electrically and (typically) mechanically connected.

100 106 101 106 110 101 100 101 106 101 101 The car-wearable deviceincludes one or more electrical componentsseparate from and electrically coupled to the receiver. These componentsare operable to detect effectsof the receiveron the device, e.g., provide measurements such as current, voltage, temperature, sound, and the like while the receiveris being driven by a signal. For example, a device battery and/or power regulation components on a main circuit board may have a digital interface to read current and voltage. Other electrical devices (e.g., power amplifiers) may have similar capability. Other componentsinclude microphones that can measure sound generated by the receiver, accelerometers that can measure vibrations from the receiver, etc.

108 101 104 112 106 104 104 101 110 106 112 A processoris operably coupled to the receiverand the one or more electrical components and configured to perform a measurement process involving sending an audio input signalto the receiver and measuring a set of frequency dependent electrical characteristicsof the one or more electrical componentsin response to the audio input signal. The audio signalmay include any combination of test tone, random noise, ambient sound measured from a microphone (not shown), etc. The receiver, due to its inherent electrical characteristics such as its impedance, will cause effectsto impact the electrical components, which are measured via the electrical characteristics.

112 114 116 116 118 101 118 101 120 100 120 122 100 The set of frequency dependent electrical characteristicsare input to a machine learning modelto determine an output. The outputof the machine learning model provides or facilitates determining a classificationof the receiver. The classificationof the receiveris used to set an operational parameterof the car-wearable device. The operational parametercan be used by software/firmwareof the car-wearable deviceto set, for example, an amplifier/receiver gain, an equalization setting, output compression limiting, etc.

2 FIG. 200 200 The term “machine-learning” can refer to a number of algorithms that train a data structure on a set of data and adapt a state of the structure based on the training to provide a specific output. Machine learning classifiers include, but aren't limited to, feedforward neural networks, recurrent neural networks, convolutional neural networks, logistic regression, K-nearest neighbors, support vector machine (SVM), Kernal SVM, naïve Bayes, and decision tree classification. In, a diagram shows a neural networkconfigured as a classifier according to an example embodiment. This neural networkis a multilayer perceptron (MLP), which is often used as a feedforward, machine learning classifier.

200 202 204 205 206 208 204 208 204 208 The neural networkincludes an input layerwhich receive numerical data representing frequency dependent electrical characteristics. Examples of these characteristic data inputs are a voltage, a current, and signal levels-of three different microphones. The inputs-are frequency dependent, therefore each input-may include a vector, with each element of the vector representing the value measured across a corresponding frequency range. These frequency dependent values may be obtained, for example, by performing a Fourier transform (FFT) on time domain data of each measurement while a test tone or other signal is played through the receiver. In other embodiments, the machine learning model may include a recurrent neural network (RNN) that performs classification directly one time domain data of the input signals, therefore does not need the input data to be transformed to the frequency domain.

202 210 202 210 210 212 214 217 200 210 The nodes of the input layerare fully connected to one or more hidden layers. Each node of the hidden layer performs an activation function on the combination of separate inputs from the input layer, and the outputs of the first hidden layermay be coupled to additional hidden layers (not shown). A last one of the hidden layersis fully connected to an output layer, which in this example is trained to distinguish between four different receiver classifications-. The illustrated networkis generally referred to as a deep neural network (DNN), in that it contains one or more hidden layersbetween the input and output.

200 202 200 The networkis trained, e.g., via backpropagation with gradient descent, to minimize an error between predicted classifications of a training data set and pre-assigned “ground truth” classifications labels associated with the training data. This is referred to as supervised training, wherein a pre-labeled set of data is used for training. In unsupervised training, a machine learning model (e.g., SVM) finds its own groupings of output classifications based on characteristics of the data learned though training. Unsupervised learning may also be used as part of the machine learning model, e.g., to find a reduced characterization of the measurements provided to input layer. Once trained, the state data of the network(e.g., node weights and biases of the hidden and output layers) can be used in a hearing device to run a similarly configured network that provides the desired classifications. After a well-performing model is trained, it could be trimmed and quantized to fit within the hearing device memory.

2 FIG. n 204 208 In Table 1 below, hyper-parameters of a neural network as shown inare provided. A neural network with similar characteristics can be implemented in other ways as described elsewhere herein and the illustrated example is not meant to be limiting. For example, not all of the inputs need to be provided over the same M-frequency buckets. If the measured values of voltage at center frequency Fdo not show significant variation across all receivers, these can be removed from the inputs, thereby reducing the size of the network and associated computing resources need to run the model. In other words, the input vectors-do not need to be of the same dimension, nor do the indices of different vectors need to correspond to the same center frequencies.

TABLE 1 Deep Neural Network Parameter Value Network Topology Multilayer perceptron, N-input vectors, each vector having M-elements corresponding to M-different frequency buckets. K-different classifications in output layer, two or more hidden layers of size ≥ M × N. Output layer normalized with a softmax function. Data format for inputs Inputs are extracted from the digitized SoC and microphone signals. These inputs can be converted to the frequency dependent data using techniques such as the Fast Fourier Transform (FFT). Activation Function ReLu activation function for all layers Learning Paradigm Supervised learning using backpropagation with gradient descent Training Dataset Measurements taken with a population of devices randomly paired with different receivers to obtain a large number (>1000) of labeled training samples Cost Function Mean squared error loss to minimize error between the correct receiver classification and the neural network result Starting Values Random values

3 FIG. 4 FIG. 400 In, a plot illustrates measured impedance as a function of frequency for a number of different receivers. Generally, these differences in impedance will result in measurably different performance of characteristics such as amplifier voltage and current, as well as differing sound pressure levels over various frequencies. In, a tableshows an example of measured values for a particular receiver that may be used to form training and operational data. Note that the eleven frequencies in the first column of the table are not evenly distributed either linearly or logarithmically, although they may be in some cases. The selection of appropriate frequencies for characterizing measurements for a population of device configurations may be determined through unsupervised machine learning.

400 The second through sixth columns of tablecould be used to form five, 11-dimensional input vectors to a machine learning model. As noted above, not all of the vectors need to have the same size, e.g., the size of some vectors can be reduced if the measurements at some frequencies provide little or no differentiation between the tested receivers. Similarly, different measurement vectors may select different frequencies to characterize, e.g., voltage and current may be selected at different frequencies than the microphone sound pressure level (SPL).

5 FIG. 2 FIG. 500 212 In, a tableshows output provided by a machine-learning model according to an example embodiment. The first column indicates a type of receiver, e.g., L=low output, which indicates an amount of nominal gain to achieve a reference output from the receiver. The second column is “1” if the class is detected, and “0” if not detected. In the second column this example, the “1” in the first row and “0” in the other rows indicates the receiver corresponds to the “L” type. Any classification scheme could be used for receiver type, e.g., a unique identifier of a class of the receiver, a model number of the receiver, a version of the receiver, etc. Each value in the second column could be provided by a different node of the output layerin.

500 212 2 FIG. The third column of the tablerepresents an left/right output from the model, e.g., provided by a single node of the output layerin. The left/right indicator is a characteristic that is independent of the type of receiver, e.g., receiver type can be changed without changing left/right location, and vice versa. The third column indicates whether the receiver is in a left or right ear position, e.g., left=“0” and right=“1.” In this example, all the rows in the third column would be the same regardless of what is in the second column. The value in the third column could be read from a single node of the output layer, so that a neural network with this example of classification output would have six output nodes, five for receiver type and one for receiver placement.

Note that the output values of a neural network may be floating point numbers, e.g., in a range between “0” and “1” inclusive. Therefore using real world inputs, the machine learning outputs may not always clearly indicate a classification as shown in this example. Various schemes known in the art may be used to deal with ambiguous outputs, e.g., assigning a “1” to the maximum value on the second row and “0” to all others, with a random choice for “1” in the case of a tie. Similarly, the left/right could be chosen based on whether the output is above or below 0.5, with random assignment if the output is exactly 0.5.

Generally, the embodiments described herein may send a well-defined, known input (or set of inputs) to the receiver. Such input may be a test tone, multiple tones, spectrally balanced noise (e.g., white noise), etc. In some case, the inputs will only be sent when the hearing device is out of car, e.g., in a charging stand, carrying case, or the like. In other cases, the input may be sent while the device is in the user's ear. This can be done discreetly, for example, by using specially crafted status tones that are occasionally emitted by the device during normal use. An example of the use of operational tones to measure device characteristics is described in U.S. Provisional Application 63/604,360, filed Nov. 30, 2023. In other cases, the machine learning model may be trained on ambient noise, such that it may be possible to classify the receiver in-ear without using specially crafted test audio.

While the input signal is being sent to the receiver, the system response is measured by the hearing device (e.g., voltage, current, and audio). The system response may be processed, e.g., converted to a frequency domain representation, scaled, averaged, etc., to provide a desired form and reduce the effects of error/noise. The processed response is fed into a classification machine learning model that detects which receiver type is being used. To train the neural network a high number of (e.g., 2000-4000) training examples with an equal amount of each receiver type could be used to ensure no biasing towards a receiver type.

In order to implement a machine learning solution for receiver identification, the existing sensors on the hearing device could be used with no additional sensor or sensing lines added to directly monitor signals from the receiver. The audio output and usage of current and voltage during the tone generation event will allow for receiver identification. Additionally, hearing aids using the more advanced power management integrated circuits (PMIC) can vary the voltage across the output driver for the receiver allowing for another axis to be added to the input matrix space.

To ensure repeatable acoustic performance, a charging cradle/case can be used to hold the hearing device during the receiver measurement. The charger or case provides a known and relatively static acoustic environment. During the classification test, the hearing device may disable the charging path between the charging case and the hearing device. This will help ensure that the charging circuitry does not influence the test results. The charging path disabling may be done by the hearing device itself and/or via a communication to the charging case.

In some cases, the hearing device may perform an initial test outside the car (e.g., inside a charging case) but may also be able to validate after being placed inside the car canal. This validation may be performed regularly to see if any anomalies are noted, or to detect situations where the user swapped out the receiver without subsequently pairing it in the charger. To ensure that the hearing device continues to correctly identify the receiver during use, an inward facing microphone can be used to determine the sound pressure within the car canal. This can be compared against a baseline, for example, to see if the receiver was swapped without running the receiver identification process, e.g., placing back in charger. If a different receiver is detected, or if an anomalous result is detected, the user may be instructed to place the hearing device back into the case to perform the complete identification check.

The trained machine learning model data can be provided to the hearing device directly, e.g., at the factory, via an audiologist. In some embodiments, the user may be able to perform an initial and/or subsequent loading of trained machine learning model data, e.g., by downloading from the Internet to a secondary device (e.g., computer, mobile phone, etc.) then transferring from the secondary device to the hearing device. The trained machine learning model data can be temporarily transferred from the secondary device to the hearing aid as needed, and later deleted from the hearing device to save memory. In some embodiments, the machine learning model can be run on the hearing device, mobile phone, accessory (charger), computer, remote server, or any other secondary device. For example, the hearing device can gather data describing the electrical characteristics and transmit the data to the secondary device for processing, after which the secondary device sends back the receiver classification. In any of these examples, the classification of the receiver determined by the machine learning model is stored into the memory as part of the measurement process and remains in the memory after the measurement process.

6 FIG. 600 601 602 603 604 In, a flowchart illustrates a method of processing sound in an car-wearable device according to an example embodiment. The method may be processor-implemented in an car-wearable device. The method involves sendingan audio input signal to a receiver of the car-wearable device. A set of frequency dependent electrical characteristics of one or more electrical components is measuredin response to the audio input signal. The one or more electrical components are separate from and electrically coupled to the receiver. The set of frequency dependent electrical characteristics are inputinto a machine learning model to determine an output. The machine learning model may run on the car-wearable device or on a secondary device. A classification of the receiver is determinedbased on the output of the machine learning model. The classification of the receiver is usedto set an operational parameter of the ear-wearable device.

7 FIG. 700 701 702 703 In, a flowchart shows method of training a machine learning model for using in an ear-wearable device according to an example embodiment. The method involves preparinga plurality of device configurations comprising equivalent ear-wearable devices that employ different receivers. The collecting of the training data involves iterations indicated by loop limit. The method involves, for each selected device configuration, sendingtest audio to a receiver of the selected device configuration. A set of electrical characteristics of one or more electrical components of the selected hearing device configuration is measuredat a corresponding set of frequencies in response to the test audio. The one or more electrical components are separate from and electrically coupled to the receiver;

704 705 706 706 707 The iterations further involve formingan ordered pair that includes: the set of electrical characteristics; and a classification of the receiver. The ordered pair is addedto the training set, after which the iterations repeat via path. The iterations complete once enough examples are measured, after which the training set is complete as indicated by line. The machine learning model is then trainedwith the training set (e.g., supervised learning with backpropagation) to predict an individual receiver classification based on an individual set of electrical characteristics measured from a fielded ear-wearable device.

8 FIG. 8 FIG. 800 800 802 800 In, a block diagram illustrates a system and ear-wearable/hearing devicein accordance with any of the embodiments disclosed herein. The hearing deviceincludes a housingconfigured to be worn in, on, or about an ear of a wearer. The hearing deviceshown incan represent a single hearing device configured for monaural or single-ear operation or one of a pair of hearing devices configured for binaural or dual-ear operation. Where two devices are used, they may be functionally equivalent, e.g., perform the same operations as least as it relates to sound processing. Functionally equivalent devices may still operate differently, e.g., having different physical form for left/right sides, having different ear canal fittings, having different sound processing settings to deal with ear-specific (left or right) pathologies, etc.

800 802 802 8 FIG. The hearing deviceshown inincludes a housingwithin or on which various components are situated or supported. The housingcan be configured for deployment on a wearer's ear (e.g., a behind-the-car device housing), within an car canal of the wearer's ear (e.g., an in-the-car, in-the-canal, invisible-in-canal, or completely-in-the-canal device housing) or both on and in a wearer's ear (e.g., a receiver-in-canal or receiver-in-the-car device housing).

800 820 822 823 820 820 822 820 823 823 838 The hearing deviceincludes a processoroperatively coupled to a main memoryand a non-volatile memory. The processorcan be implemented as one or more of a multi-core processor, a digital signal processor (DSP), a microprocessor, a programmable controller, a general-purpose computer, a special-purpose computer, a hardware controller, a software controller, a combined hardware and software device, such as a programmable logic controller, and a programmable logic device (e.g., FPGA, ASIC). The processorcan include or be operatively coupled to main memory, such as RAM (e.g., DRAM, SRAM). The processorcan include or be operatively coupled to non-volatile (persistent) memory, such as ROM, EPROM, EEPROM or flash memory. As will be described in detail hereinbelow, the non-volatile memoryis configured to store instructions (e.g., in module) that provide functionality described elsewhere herein.

800 820 830 832 830 830 802 The hearing deviceincludes an audio processing facility (also referred to as an audio processor circuit) operably coupled to, or incorporating, the processor. The audio processing facility includes audio signal processing circuitry (e.g., analog front-end, analog-to-digital converter, digital-to-analog converter, DSP, and various analog and digital filters), a microphone arrangement, and a receiver(e.g., acoustic/vibration transducer, loudspeaker, receiver, bone conduction transducer, motor actuator). The microphone arrangementcan include two or more discrete microphones or a microphone array(s) (e.g., configured for microphone array beamforming). Each of the microphones of the microphone arrangementcan be situated at different locations of the housing. It is understood that the term microphone used herein can refer to a single microphone or multiple microphones unless specified otherwise.

832 832 The receiverproduces amplified sound inside of the car canal. For purposes of this disclosure, “amplified” sound refers to electronically reproduced sound, which typically involves the use of an amplifier/driver to drive the receiver. Amplified sound does not necessarily imply an increase in sound pressure level of ambient sounds relative to what would be experienced with the hearing device removed. In some cases, the amplified sound may result in an overall sound pressure level similar to ambient, e.g., where an equalization curve is applied to affect a small frequency range. In other cases, amplified sound can reduce the sound pressure level in the ear, e.g., via active noise cancellation.

800 827 820 827 800 827 800 The hearing devicemay also include a user control interfaceoperatively coupled to the processor. The user control interfaceis configured to receive an input from the wearer of the hearing device. The input from the wearer can be any type of user input, such as a touch input, a gesture input, and/or a voice input. The user control interfacemay be configured to receive an input from the wearer of the hearing device.

800 838 820 838 800 838 832 839 820 832 839 The hearing devicealso includes a receiver-identifying, ML modeloperable via the processor. The modulecan be implemented in software, hardware (e.g., specialized neural network logic circuitry, general purpose processor), or a combination of hardware and software. During operation of the hearing device, the ML modulecan be used to identify a classification of the receiverby measuring electrical characteristics of the hardware, as indicated by hardware interface. The processorinitiates the classification process by sending a signal (e.g., test tone, ambient sound) to the receiverand measuring the effects via the hardware interface.

834 800 834 838 The hearing device may include other sensors, such as an IMUto determine an operating context of the hearing device, e.g., in-ear, out-of-car, etc., which can affect how the sound is analyzed and processed. The IMUcan also be used to provide inputs to the ML model, such as determining low frequencies via accelerometers, detecting system disturbances, etc.

800 836 836 800 836 The hearing devicecan include one or more communication devices. For example, the one or more communication devicescan include one or more radios coupled to one or more antenna arrangements that conform to an IEEE 802.8 (e.g., Wi-Fi®) or Bluetooth® (e.g., BLE, Bluetooth® 4.2, 5.0, 5.1, 5.2 or later) specification, for example. In addition, or alternatively, the hearing devicecan include a near-field magnetic induction (NFMI) sensor (e.g., an NFMI transceiver coupled to a magnetic antenna) for effecting short-range communications (e.g., car-to-car communications, car-to-kiosk communications). The communications devicemay also include wired communications, e.g., universal serial bus (USB) and the like.

836 800 804 805 804 804 809 804 811 The communication deviceis operable to allow the hearing deviceto communicate with an external computing device, e.g., a mobile devicesuch as smartphone, laptop computer, table, etc. The external computing devicemay include a user device and/or a device usable by a clinician in a clinical setting, such as a desktop computer, test apparatus, etc. The external computing devicemay include a second hearing device, e.g. part of a pair of corresponding devices for both ears of the user. The external computing devicemay also include a charging case.

804 806 836 804 808 810 807 804 800 838 807 The external computing deviceincludes a communications devicethat is compatible with the communications devicefor point-to-point or network communications. The external computing deviceincludes its own processorand memory, the latter which may encompass both volatile and non-volatile memory. A user interfacefacilitates interactions between the external computing deviceand the hearing device, including access to settings that affect the ML model. The user interfacemay, for example, allow a user check on the automatic receiver classification, e.g., compare the machine-identification with a physical identification (e.g., code, category, or model number printed on the device).

800 800 824 800 824 826 826 802 811 800 811 8 FIG. The hearing devicealso includes a power source, which can be a conventional battery, a rechargeable battery (e.g., a lithium-ion battery), or a power source comprising a supercapacitor. In the embodiment shown in, the hearing deviceincludes a rechargeable power sourcewhich is operably coupled to power management circuitry for supplying power to various components of the hearing device. The rechargeable power sourceis coupled to charging circuitry. The charging circuitryis electrically coupled to charging contacts on the housingwhich are configured to electrically couple to corresponding charging contacts of a chargerwhen the hearing deviceis placed in the charger.

The term ‘hearing device’ of the present disclosure may refer to a wide variety of car-level electronic devices that can aid a person with or without impaired hearing. This includes devices that can produce processed sound for persons with normal hearing, such as noise addition/cancellation to treat misophonia, or wireless earbuds for electronic sound playback. Hearing devices include, but are not limited to, behind-the-car (BTE), in-the-car (ITE), in-the-canal (ITC), invisible-in-canal (IIC), receiver-in-canal (RIC), receiver-in-the-car (RITE) or completely-in-the-canal (CIC) type hearing devices or some combination of the above. Throughout this disclosure, reference is made to a “hearing device” or “car-wearable device,” which is understood to refer to a system comprising a single left car.

This document discloses numerous example embodiments, including but not limited to the following:

Example 1 is an car-wearable device, comprising: a receiver that outputs sound into an car canal of a user in response to an electrical input signal; one or more electrical components separate from and electrically coupled to the receiver; and a processor operably coupled to the receiver and the one or more electrical components and configured to perform a measurement process comprising: sending an audio input signal to the receiver; measuring a set of frequency dependent electrical characteristics of the one or more electrical components in response to the audio input signal; inputting the set of frequency dependent electrical characteristics into a machine learning model to determine an output; determining a classification of the receiver based on the output of the machine learning model; and using the classification of the receiver to set an operational parameter of the car-wearable device.

Example 2 includes the car-wearable device of example 1, wherein the audio input comprises a test tone or signal. Example 3 includes the car-wearable device of example 1 or 2, wherein the audio input comprises ambient sound. Example 4 includes the car-wearable device of any preceding example, wherein the one or more electrical components comprise one or both of a battery and a power management circuit. Example 5 includes the car-wearable device of example 4, wherein the set of electrical characteristics comprises a voltage of the battery. Example 6 includes the car-wearable device of example 4 or 5, wherein the set of electrical characteristics comprises a discharge current of the battery.

Example 7 includes the car-wearable device of any preceding example, wherein the one or more electrical components comprise one or more microphones, and wherein the set of electrical characteristics comprises respective sound pressure levels of the one or more microphones. Example 8 includes the car-wearable device of any preceding example, wherein the operational parameter comprises one or more of receiver gain and output compression limiting.

Example 9 includes the ear-wearable device of any preceding example, wherein the measurement process is performed outside the ear canal of the user. Example 10 includes the ear-wearable device of example 9, wherein the processor is further configured to detect that the ear-wearable device is in a charging apparatus outside the ear of the user and perform the measurement process in response thereto. Example 11 includes the ear-wearable device of example 10, wherein the measurement process further comprises disabling a charging path between the ear-wearable device and the charging apparatus during the measurement process.

Example 12 includes the ear-wearable device of example 9, 10, or 11, wherein after the measurement process is complete and the ear-wearable device is in the ear canal of the user, measuring a sound pressure level of sound received an inward facing microphone of the ear-wearable device to validate the classification of the receiver.

1 12 Example 13 includes the ear-wearable device of any preceding example, wherein the machine learning model comprises a multilayer, feedforward, neural network. Example 14 includes the ear-wearable device of any one of claims-, wherein the machine learning model comprises one or more of: a logistic regression, K-nearest neighbors, support vector machine (SVM), Kernal SVM, naïve Bayes, and decision tree classification.

Example 15 includes the ear-wearable device of any preceding example, wherein the classification of the receiver comprises one or more of a unique identifier of a class of the receiver, a model number of the receiver, a version of the receiver, and a left/right side indicator. Example 16 includes the ear-wearable device of any preceding example, wherein the classification of the receiver is based on amount of nominal gain to achieve a reference output from the receiver. Example 17 includes the ear-wearable device of any preceding example, wherein the processor is further configured to load the machine learning model into a memory of the ear-wearable device from an external user device before the measurement process. Example 18 includes the ear-wearable device of example 17, wherein the processor is further configured to delete the machine learning model from memory after the measurement process, wherein the classification of the receiver is stored into the memory as part of the measurement process and remains in the memory after the measurement process.

Example 19 is a method of processing sound in an car-wearable device, comprising: sending an audio input signal to a receiver of the car-wearable device; measuring a set of frequency dependent electrical characteristics of one or more electrical components in response to the audio input signal, the one or more electrical components being separate from and electrically coupled to the receiver; inputting the set of frequency dependent electrical characteristics into a machine learning model to determine an output; determining a classification of the receiver based on the output of the machine learning model; and using the classification of the receiver to set an operational parameter of the car-wearable device.

Example 20 includes the method of example 19, wherein the measurement process is performed outside an car canal of a user. Example 21 includes the method of example 20, further comprising detecting that the car-wearable device is in a charging apparatus outside the car of the user and perform the measurement process in response thereto. Example 22 includes the method of example 21, wherein the measurement process further comprises disabling a charging path between the car-wearable device and the charging apparatus during the measurement process. Example 23 includes the method of any one of examples 20-22, wherein after the measurement process is complete and the car-wearable device is in the car of the user, measuring a sound pressure level of sound received an inward facing microphone of the car-wearable device to validate the classification of the receiver.

Example 24 includes the method of any preceding method example, further comprising load the machine learning model into a memory of the car-wearable device from an external user device before the measurement process. Example 25 includes the method of example 24, further comprising deleting the machine learning model from memory after the measurement process, wherein the classification of the receiver is stored into the memory as part of the measurement process and remains in the memory after the measurement process.

Example 25 is a method of training a machine learning model for using in an car-wearable device, comprising: collecting training data from a plurality of device configurations comprising equivalent ear-wearable devices that employ different receivers, the collecting of the training data comprising, for each selected device configuration from the plurality of device configurations: sending test audio to a receiver of the selected device configuration; measuring a set of electrical characteristics of one or more electrical components of the selected device configuration at a corresponding set of frequencies in response to the test audio, the one or more electrical components separate from and electrically coupled to the receiver; forming an ordered pair comprising: the set of electrical characteristics; and a classification of the receiver; and adding the ordered pair to the training set; and training the machine learning model with the training set to predict an individual receiver classification based on an individual set of electrical characteristics measured from a fielded car-wearable device.

Example 27 includes the method of example 26, wherein the machine learning model comprises a multilayer, feedforward, neural network. Example 28 includes the method of example 26, wherein the machine learning model comprises one or more of: a logistic regression, K-nearest neighbors, support vector machine (SVM), kernel SVM, naïve Bayes, and decision tree classification.

Although reference is made herein to the accompanying set of drawings that form part of this disclosure, one of at least ordinary skill in the art will appreciate that various adaptations and modifications of the embodiments described herein are within, or do not depart from, the scope of this disclosure. For example, aspects of the embodiments described herein may be combined in a variety of ways with each other. Therefore, it is to be understood that, within the scope of the appended claims, the claimed invention may be practiced other than as explicitly described herein.

All references and publications cited herein are expressly incorporated herein by reference in their entirety into this disclosure, except to the extent they may directly contradict this disclosure. Unless otherwise indicated, all numbers expressing feature sizes, amounts, and physical properties used in the specification may be understood as being modified either by the term “exactly” or “about.” Accordingly, unless indicated to the contrary, the numerical parameters set forth in the foregoing specification are approximations that can vary depending upon the desired properties sought to be obtained by those skilled in the art utilizing the teachings disclosed herein or, for example, within typical ranges of experimental error.

The recitation of numerical ranges by endpoints includes all numbers subsumed within that range (e.g., 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.80, 4, and 5) and any range within that range. Herein, the terms “up to” or “no greater than” a number (e.g., up to 50) includes the number (e.g., 50), and the term “no less than” a number (e.g., no less than 5) includes the number (e.g., 5).

The terms “coupled” or “connected” refer to elements being attached to each other either directly (in direct contact with each other) or indirectly (having one or more elements between and attaching the two elements). Either term may be modified by “operatively” and “operably,” which may be used interchangeably, to describe that the coupling or connection is configured to allow the components to interact to carry out at least some functionality (for example, a radio chip may be operably coupled to an antenna element to provide a radio frequency electric signal for wireless communication).

Terms related to orientation, such as “top,” “bottom,” “side,” and “end,” are used to describe relative positions of components and are not meant to limit the orientation of the embodiments contemplated. For example, an embodiment described as having a “top” and “bottom” also encompasses embodiments thereof rotated in various directions unless the content clearly dictates otherwise.

Reference to “one embodiment,” “an embodiment,” “certain embodiments,” or “some embodiments,” etc., means that a particular feature, configuration, composition, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. Thus, the appearances of such phrases in various places throughout are not necessarily referring to the same embodiment of the disclosure. Furthermore, the particular features, configurations, compositions, or characteristics may be combined in any suitable manner in one or more embodiments.

The words “preferred” and “preferably” refer to embodiments of the disclosure that may afford certain benefits, under certain circumstances. However, other embodiments may also be preferred, under the same or other circumstances. Furthermore, the recitation of one or more preferred embodiments does not imply that other embodiments are not useful and is not intended to exclude other embodiments from the scope of the disclosure.

As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” encompass embodiments having plural referents, unless the content clearly dictates otherwise. As used in this specification and the appended claims, the term “or” is generally employed in its sense including “and/or” unless the content clearly dictates otherwise.

As used herein, “have,” “having,” “include,” “including,” “comprise,” “comprising” or the like are used in their open-ended sense, and generally mean “including, but not limited to.” It will be understood that “consisting essentially of,” “consisting of,” and the like are subsumed in “comprising,” and the like. The term “and/or” means one or all of the listed elements or a combination of at least two of the listed elements.

The phrases “at least one of,” “comprises at least one of,” and “one or more of” followed by a list refers to any one of the items in the list and any combination of two or more items in the list.